Expression and regulatory roles of lncRNAs in G-CIMP-low vs G-CIMP-high Glioma: an in-silico analysis

Expression of the checkpoint kinase BUB1 is a predictor of response to cancer therapies

The identification of clinically-relevant biomarkers is of upmost importance for the management of cancer, from diagnosis to treatment choices. We performed a pan-cancer analysis of the mitotic checkpoint budding uninhibited by benzimidazole 1 gene BUB1, in the attempt to ascertain its diagnostic and prognostic values, specifically in the context of drug response. BUB1 was found to be overexpressed in the majority of cancers, and particularly elevated in clinically aggressive molecular subtypes. Its expression was correlated with clinico-phenotypic features, notably tumour staging, size, invasion, hypoxia, and stemness. In terms of prognostic value, the expression of BUB1 bore differential clinical outcomes depending on the treatment administered in TCGA cancer cohorts, suggesting sensitivity or resistance, depending on the expression levels. We also integrated in vitro drug sensitivity data from public projects based on correlation between drug efficacy and BUB1 expression to produce a list of candidate compounds with differential responses according to BUB1 levels. Gene Ontology enrichment analyses revealed that BUB1 overexpression in cancer is associated with biological processes related to mitosis and chromosome segregation machinery, reflecting the mechanisms of action of drugs with a differential effect based on BUB1 expression.

ncRNA-mediated upregulation of FAM83A is associated with poor prognosis and immune infiltration in pancreatic cancer

INTRODUCTION

Malignant pancreatic cancer has poor long-term survival. Increasing evidence shows that FAM83A (family with sequence similarity 83 member A) plays a vital role in tumorigenesis and malignant progression in some human cancer types. The present study explored the potential mechanism of FAM83A in improving the prognosis of pancreatic cancer patients.

METHODS

Transcriptomic and clinical data from patients were obtained from The Cancer Genome Atlas while FAM83A expression was measured in tumorous pancreatic tissue compared with normal controls by quantitative real-time PCR and immunohistochemistry.

RESULTS

FAM83A is a vital prognostic indicator and potential oncogene in pancreatic cancer via pan-cancer analysis. In silico analysis revealed that AL049555.1/hsa-miR-129-5p axis was the pivotal upstream ncRNA- mediated pathway of FAM83A in pancreatic cancer. Furthermore, FAM83A expression was related to immune cell infiltration through vital immune-related genes including programmed cell death 1 (PDCD1), and tumorigenesis through common mutation genes including KRAS protooncogene GTPase (KRAS), and SMAD family member 4 (SMAD4). In summary, ncRNA-mediated upregulation of FAM83A is associated with poor long-term survival and immune cell infiltration in pancreatic cancer.

DISCUSSION

FAM83A may be used as a novel survival-related and immune-related biomarker. This information suggests that FAM83A may be a novel therapeutic target for combined or individual treatment for patients with pancreatic cancer.

TP53I13 promotes metastasis in glioma via macrophages, neutrophils, and fibroblasts and is a potential prognostic biomarker

Background

TP53I13 is a protein coding tumor suppression gene encoded by the tumor protein p53. Overexpression of TP53I13 impedes tumor cell proliferation. Nevertheless, TP53I13 role and expression in the emergence and progression of glioma (low-grade glioma and glioblastoma) are yet to be identified. Thus, we aim to use comprehensive bioinformatics analyses to investigate TP53I13 and its prognostic value in gliomas.

Methods

Multiple databases were consulted to evaluate and assess the expression of TP53I13, such as the Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), GeneMANIA, and Gene Expression Profiling Interactive. TP53I13 expression was further explored using immunohistochemistry (IHC) and multiplex immunohistochemistry (mIHC). Through Gene Set Enrichment Analysis (GSEA), the biological functions of TP53I13 and metastatic processes associated with it were studied.

Results

The expression of TP53I13 was higher in tumor samples compared to normal samples. In samples retrieved from the TCGA and CGGA databases, high TP53I13 expression was associated with poor survival outcomes. The analysis of multivariate Cox showed that TP53I13 might be an independent prognostic marker of glioma. It was also found that increased expression of TP53I13 was significantly correlated with PRS type, status, 1p/19q codeletion status, IDH mutation status, chemotherapy, age, and tumor grade. According to CIBERSORT (Cell-type Identification by Estimating Relative Subsets of RNA Transcript), the expression of TP53I13 correlates with macrophages, neutrophils, and dendritic cells. GSEA shows a close correlation between TP53I13 and p53 signaling pathways, DNA replication, and the pentose phosphate pathway.

Conclusion

Our results reveal a close correlation between TP53I13 and gliomas. Further, TP53I13 expression could affect the survival outcomes in glioma patients. In addition, TP53I13 was an independent marker that was crucial in regulating the infiltration of immune cells into tumors. As a result of these findings, TP53I13 might represent a new biomarker of immune infiltration and prognosis in patients with gliomas.

Regulatory pattern of abnormal promoter CpG island methylation in the glioblastoma multiforme classification

Glioblastoma (GBM) is characterized by extensive genetic and phenotypic heterogeneity. However, it remains unexplored primarily how CpG island methylation abnormalities in promoter mediate glioblastoma typing. First, we presented a multi-omics scale map between glioblastoma sample clusters constructed based on promoter CpG island (PCGI) methylation-driven genes, using datasets including methylation profiles, expression profiles, and single-cell sequencing data from multiple highly annotated public clinical cohorts. Second, we identified differences in the tumor microenvironment between the two glioblastoma sample clusters and resolved key signaling pathways between cell clusters at the single-cell level based on comprehensive comparative analyses to investigate the reasons for survival differences between two of these clusters. Finally, we developed a diagnostic map and a prediction model for glioblastoma, and compared theoretical differences of drug sensitivity between two glioblastoma sample clusters. In summary, this study established a classification system for dissecting promoter CpG island methylation heterogeneity in glioblastoma and provides a new perspective for the diagnosis and treatment of glioblastoma.

Deciphering the etiology and role in oncogenic transformation of the CpG island methylator phenotype: a pan-cancer analysis

Numerous cancer types have shown to present hypermethylation of CpG islands, also known as a CpG island methylator phenotype (CIMP), often associated with survival variation. Despite extensive research on CIMP, the etiology of this variability remains elusive, possibly due to lack of consistency in defining CIMP. In this work, we utilize a pan-cancer approach to further explore CIMP, focusing on 26 cancer types profiled in the Cancer Genome Atlas (TCGA). We defined CIMP systematically and agnostically, discarding any effects associated with age, gender or tumor purity. We then clustered samples based on their most variable DNA methylation values and analyzed resulting patient groups. Our results confirmed the existence of CIMP in 19 cancers, including gliomas and colorectal cancer. We further showed that CIMP was associated with survival differences in eight cancer types and, in five, represented a prognostic biomarker independent of clinical factors. By analyzing genetic and transcriptomic data, we further uncovered potential drivers of CIMP and classified them in four categories: mutations in genes directly involved in DNA demethylation; mutations in histone methyltransferases; mutations in genes not involved in methylation turnover, such as KRAS and BRAF; and microsatellite instability. Among the 19 CIMP-positive cancers, very few shared potential driver events, and those drivers were only IDH1 and SETD2 mutations. Finally, we found that CIMP was strongly correlated with tumor microenvironment characteristics, such as lymphocyte infiltration. Overall, our results indicate that CIMP does not exhibit a pan-cancer manifestation; rather, general dysregulation of CpG DNA methylation is caused by heterogeneous mechanisms.